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Cruz EO, Sakowitz S, Mallick S, Le N, Chervu N, Bakhtiyar SS, Benharash P. Application of machine learning to predict in-hospital mortality after transcatheter mitral valve repair. Surgery 2024:S0039-6060(24)00490-2. [PMID: 39122592 DOI: 10.1016/j.surg.2024.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 06/14/2024] [Accepted: 07/03/2024] [Indexed: 08/12/2024]
Abstract
INTRODUCTION Transcatheter mitral valve repair offers a minimally invasive treatment option for patients at high risk for traditional open repair. We sought to develop dynamic machine-learning risk prediction models for in-hospital mortality after transcatheter mitral valve repair using a national cohort. METHODS All adult hospitalization records involving transcatheter mitral valve repair were identified in the 2016-2020 Nationwide Readmissions Database. As a result of initial class imbalance, undersampling of the majority class and subsequent oversampling of the minority class using Synthetic Minority Oversampling TEchnique were employed in each cross-validation training fold. Machine-learning models were trained to predict patient mortality after transcatheter mitral valve repair and compared with traditional logistic regression. Shapley additive explanations plots were also developed to understand the relative impact of each feature used for training. RESULTS Among 2,450 patients included for analysis, the in-hospital mortality rate was 1.8%. Naïve Bayes and random forest models were the best at predicting transcatheter mitral valve repair postoperative mortality, with an area under the receiver operating characteristic curve of 0.83 ± 0.05 and 0.82 ± 0.04, respectively. Both models demonstrated superior ability to predict mortality relative to logistic regression (P < .001 for both). Medicare insurance coverage, comorbid liver disease, congestive heart failure, renal failure, and previous coronary artery bypass grafting were associated with greater predicted likelihood of in-hospital mortality, whereas elective surgery and private insurance coverage were linked with lower odds of mortality. CONCLUSION Machine-learning models significantly outperformed traditional regression methods in predicting in-hospital mortality after transcatheter mitral valve repair. Furthermore, we identified key patient factors and comorbidities linked with greater postoperative mortality. Future work and clinical validation are warranted to continue improving risk assessment in transcatheter mitral valve repair .
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Affiliation(s)
- Emma O Cruz
- Division of Cardiac Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA; Department of Computer Science, Stanford University, Palo Alto, CA
| | - Sara Sakowitz
- Division of Cardiac Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA. https://www.twitter.com/sarasakowitz
| | - Saad Mallick
- Division of Cardiac Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Nguyen Le
- Division of Cardiac Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Nikhil Chervu
- Division of Cardiac Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Syed Shahyan Bakhtiyar
- Division of Cardiac Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA; Department of Surgery, University of Colorado Denver, Aurora, CO. https://www.twitter.com/Aortologist
| | - Peyman Benharash
- Division of Cardiac Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA.
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Jaltotage B, Lu J, Dwivedi G. Use of Artificial Intelligence Including Multimodal Systems to Improve the Management of Cardiovascular Disease. Can J Cardiol 2024:S0828-282X(24)00566-X. [PMID: 39038650 DOI: 10.1016/j.cjca.2024.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 07/24/2024] Open
Abstract
The rising prevalence of cardiovascular disease presents an escalating challenge for current health services, which are grappling with increasing demands. Innovative changes are imperative to sustain the delivery of high-quality patient care. Recent technologic advances have resulted in the emergence of artificial intelligence as a viable solution. Advanced algorithms are now capable of performing complex analysis of large volumes of data rapidly and with exceptional accuracy. Multimodality artificial intelligence systems handle a diverse range of data including images, text, video, and audio. Compared with single-modality systems, multimodal artificial intelligence systems appear to hold promise for enhancing overall performance and enabling smoother integration into existing workflows. Such systems can empower physicians with clinical decision support and enhanced efficiency. Owing to the complexity of the field, however, truly multimodal artificial intelligence is still scarce in the management of cardiovascular disease. This article aims to cover current research, emerging trends, and the future utilisation of artificial intelligence in the management of cardiovascular disease, with a focus on multimodality systems.
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Affiliation(s)
- Biyanka Jaltotage
- Department of Cardiology, Fiona Stanley Hospital, Perth, Western Australia, Australia
| | - Juan Lu
- Harry Perkins Institute of Medical Research, Perth, Western Australia, Australia; School of Medicine, University of Western Australia, Perth, Western Australia, Australia
| | - Girish Dwivedi
- Department of Cardiology, Fiona Stanley Hospital, Perth, Western Australia, Australia; Harry Perkins Institute of Medical Research, Perth, Western Australia, Australia; School of Medicine, University of Western Australia, Perth, Western Australia, Australia.
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Alie MS, Negesse Y, Kindie K, Merawi DS. Machine learning algorithms for predicting COVID-19 mortality in Ethiopia. BMC Public Health 2024; 24:1728. [PMID: 38943093 PMCID: PMC11212371 DOI: 10.1186/s12889-024-19196-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 06/19/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms offer a promising approach to data-driven prediction of clinical outcomes, surpassing traditional statistical modeling. Leveraging machine learning (ML) algorithms could potentially provide a solution for predicting mortality in hospitalized COVID-19 patients in Ethiopia. Therefore, the aim of this study is to develop and validate machine-learning models for accurately predicting mortality in COVID-19 hospitalized patients in Ethiopia. METHODS Our study involved analyzing electronic medical records of COVID-19 patients who were admitted to public hospitals in Ethiopia. Specifically, we developed seven different machine learning models to predict COVID-19 patient mortality. These models included J48 decision tree, random forest (RF), k-nearest neighborhood (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), eXtreme gradient boosting (XGBoost), and logistic regression (LR). We then compared the performance of these models using data from a cohort of 696 patients through statistical analysis. To evaluate the effectiveness of the models, we utilized metrics derived from the confusion matrix such as sensitivity, specificity, precision, and receiver operating characteristic (ROC). RESULTS The study included a total of 696 patients, with a higher number of females (440 patients, accounting for 63.2%) compared to males. The median age of the participants was 35.0 years old, with an interquartile range of 18-79. After conducting different feature selection procedures, 23 features were examined, and identified as predictors of mortality, and it was determined that gender, Intensive care unit (ICU) admission, and alcohol drinking/addiction were the top three predictors of COVID-19 mortality. On the other hand, loss of smell, loss of taste, and hypertension were identified as the three lowest predictors of COVID-19 mortality. The experimental results revealed that the k-nearest neighbor (k-NN) algorithm outperformed than other machine learning algorithms, achieving an accuracy of 95.25%, sensitivity of 95.30%, precision of 92.7%, specificity of 93.30%, F1 score 93.98% and a receiver operating characteristic (ROC) score of 96.90%. These findings highlight the effectiveness of the k-NN algorithm in predicting COVID-19 outcomes based on the selected features. CONCLUSION Our study has developed an innovative model that utilizes hospital data to accurately predict the mortality risk of COVID-19 patients. The main objective of this model is to prioritize early treatment for high-risk patients and optimize strained healthcare systems during the ongoing pandemic. By integrating machine learning with comprehensive hospital databases, our model effectively classifies patients' mortality risk, enabling targeted medical interventions and improved resource management. Among the various methods tested, the K-nearest neighbors (KNN) algorithm demonstrated the highest accuracy, allowing for early identification of high-risk patients. Through KNN feature identification, we identified 23 predictors that significantly contribute to predicting COVID-19 mortality. The top five predictors are gender (female), intensive care unit (ICU) admission, alcohol drinking, smoking, and symptoms of headache and chills. This advancement holds great promise in enhancing healthcare outcomes and decision-making during the pandemic. By providing services and prioritizing patients based on the identified predictors, healthcare facilities and providers can improve the chances of survival for individuals. This model provides valuable insights that can guide healthcare professionals in allocating resources and delivering appropriate care to those at highest risk.
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Affiliation(s)
- Melsew Setegn Alie
- Department Public Health, School of Public Health, College of Medicine and Health Science, Mizan-Tepi University, Mizan-Aman, Ethiopia.
| | - Yilkal Negesse
- Department of Public Health, College of Medicine and Health Science, Debre Markos University, Gojjam, Ethiopia
| | - Kassa Kindie
- Department Nursing, College of Medicine and Health Science, Mizan-Tepi University, Mizan-Aman, Ethiopia
| | - Dereje Senay Merawi
- Department of Information Technology, Faculty of Technology, Debre Tabor University, Gonder, Ethiopia
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Cho Y, Lee HK, Kim J, Yoo KB, Choi J, Lee Y, Choi M. Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study. BMC Infect Dis 2024; 24:466. [PMID: 38698304 PMCID: PMC11067145 DOI: 10.1186/s12879-024-09358-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 04/26/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND Hospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings. AIM This study aimed to investigate factors related to HAI, develop predictive models, and subsequently compare them to identify the best performing machine learning algorithm for predicting the occurrence of HAI. METHODS This retrospective observational study was conducted in 2022 and included 111 HAI and 73,748 non-HAI patients from the 2011-2012 and 2019-2020 influenza seasons. General characteristics, comorbidities, vital signs, laboratory and chest X-ray results, and room information within the electronic medical record were analysed. Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) techniques were used to construct the predictive models. Employing randomized allocation, 80% of the dataset constituted the training set, and the remaining 20% comprised the test set. The performance of the developed models was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), the count of false negatives (FN), and the determination of feature importance. RESULTS Patients with HAI demonstrated notable differences in general characteristics, comorbidities, vital signs, laboratory findings, chest X-ray result, and room status compared to non-HAI patients. Among the developed models, the RF model demonstrated the best performance taking into account both the AUC (83.3%) and the occurrence of FN (four). The most influential factors for prediction were staying in double rooms, followed by vital signs and laboratory results. CONCLUSION This study revealed the characteristics of patients with HAI and emphasized the role of ventilation in reducing influenza incidence. These findings can aid hospitals in devising infection prevention strategies, and the application of machine learning-based predictive models especially RF can enable early intervention to mitigate the spread of influenza in healthcare settings.
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Affiliation(s)
- Younghee Cho
- College of Nursing, Yonsei University, Seoul, Republic of Korea
- Department of Digital Health, Samsung SDS, Seoul, Republic of Korea
| | - Hyang Kyu Lee
- College of Nursing, Yonsei University, Seoul, Republic of Korea
| | - Joungyoun Kim
- College of Engineering, University of Seoul, Seoul, Republic of Korea
| | - Ki-Bong Yoo
- Division of Health Administration, Yonsei University, Wonju, Republic of Korea
| | - Jongrim Choi
- College of Nursing, Keimyung University, Daegu, Republic of Korea
| | - Yongseok Lee
- Department of Digital Health, Samsung SDS, Seoul, Republic of Korea
| | - Mona Choi
- College of Nursing, Yonsei University, Seoul, Republic of Korea.
- Mo-Im Kim Nursing Research Institute, College of Nursing, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
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Kathavarayan Ramu S, Agrawal A, Shekhar S, Bansal A, Isogai T, Yun J, Reed GW, Puri R, Krishnaswamy A, Kapadia SR. MitraCox Score Predicts In-Hospital Mortality in Patients Admitted for Transcatheter Edge-to-Edge Mitral Valve Repair. Am J Cardiol 2023; 207:39-47. [PMID: 37722200 DOI: 10.1016/j.amjcard.2023.08.160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/20/2023]
Abstract
As mitral valve transcatheter edge-to-edge repair (M-TEER) is evolving as an effective treatment for high-risk surgical patients with mitral regurgitation, there is a pressing need for cardiologists to optimize resources through risk stratification of in-hospital mortality for this patient population. Although current risk-prediction models have been shown to predict adverse outcomes with reasonable accuracy, models trained using the US nationwide population are lacking. This study aimed to identify clinical, demographic, and procedural features that predict in-hospital mortality, and to derive and validate an in-hospital mortality risk-prediction tool in patients who have undergone M-TEER. A total of 9,373 admissions from the Nationwide Readmissions Database of patients who underwent M-TEER between 2015 and 2018 were used to develop and validate the model. We first performed least absolute shrinkage and selection operator (LASSO) regularization of Cox regression (Coxnet) that is 10-fold cross-validated. The non-zero coefficients were multiplied with the respective values of each observation of the predictors to build the scoring formula. Out of 9,373 admissions, 196 patients (2%) died in-hospital during index admission. In descending order, the top variables that were most predictive of in-hospital mortality were higher age, presence of fluid and electrolyte disturbance, and large metropolitan location of the hospital. The validation C-statistic of the MitraCox score was 0.82. Using X-tile software (Yale School of Medicine, New Haven, Connecticut), 2 cutoffs of the score were determined on the basis of in-hospital mortality and length of stay, and the survival of the population was classified into 3 risk groups: low, intermediate, and high. The scoring system deployed online as a web-based calculator can be accessed at https://kathavs.shinyapps.io/Mitracox_Kapadia/. In conclusion, MitraCox score is easy to calculate and predicts in-hospital mortality depending on length of stay in a dynamic manner.
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Affiliation(s)
| | - Ankit Agrawal
- Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland Clinic, Cleveland, Ohio
| | - Shashank Shekhar
- Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland Clinic, Cleveland, Ohio
| | - Agam Bansal
- Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland Clinic, Cleveland, Ohio
| | - Toshiaki Isogai
- Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland Clinic, Cleveland, Ohio
| | - James Yun
- Department of Thoracic and Cardiovascular Surgery, Heart, Vascular & Thoracic Institute, Cleveland Clinic, Cleveland, Ohio
| | - Grant W Reed
- Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland Clinic, Cleveland, Ohio
| | - Rishi Puri
- Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland Clinic, Cleveland, Ohio
| | - Amar Krishnaswamy
- Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland Clinic, Cleveland, Ohio
| | - Samir R Kapadia
- Department of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland Clinic, Cleveland, Ohio.
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6
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Samant S, Bakhos JJ, Wu W, Zhao S, Kassab GS, Khan B, Panagopoulos A, Makadia J, Oguz UM, Banga A, Fayaz M, Glass W, Chiastra C, Burzotta F, LaDisa JF, Iaizzo P, Murasato Y, Dubini G, Migliavacca F, Mickley T, Bicek A, Fontana J, West NEJ, Mortier P, Boyers PJ, Gold JP, Anderson DR, Tcheng JE, Windle JR, Samady H, Jaffer FA, Desai NR, Lansky A, Mena-Hurtado C, Abbott D, Brilakis ES, Lassen JF, Louvard Y, Stankovic G, Serruys PW, Velazquez E, Elias P, Bhatt DL, Dangas G, Chatzizisis YS. Artificial Intelligence, Computational Simulations, and Extended Reality in Cardiovascular Interventions. JACC Cardiovasc Interv 2023; 16:2479-2497. [PMID: 37879802 DOI: 10.1016/j.jcin.2023.07.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 07/11/2023] [Accepted: 07/13/2023] [Indexed: 10/27/2023]
Abstract
Artificial intelligence, computational simulations, and extended reality, among other 21st century computational technologies, are changing the health care system. To collectively highlight the most recent advances and benefits of artificial intelligence, computational simulations, and extended reality in cardiovascular therapies, we coined the abbreviation AISER. The review particularly focuses on the following applications of AISER: 1) preprocedural planning and clinical decision making; 2) virtual clinical trials, and cardiovascular device research, development, and regulatory approval; and 3) education and training of interventional health care professionals and medical technology innovators. We also discuss the obstacles and constraints associated with the application of AISER technologies, as well as the proposed solutions. Interventional health care professionals, computer scientists, biomedical engineers, experts in bioinformatics and visualization, the device industry, ethics committees, and regulatory agencies are expected to streamline the use of AISER technologies in cardiovascular interventions and medicine in general.
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Affiliation(s)
- Saurabhi Samant
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Jules Joel Bakhos
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Wei Wu
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Shijia Zhao
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Ghassan S Kassab
- California Medical Innovations Institute, San Diego, California, USA
| | - Behram Khan
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Anastasios Panagopoulos
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Janaki Makadia
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Usama M Oguz
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Akshat Banga
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Muhammad Fayaz
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - William Glass
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Claudio Chiastra
- PoliTo(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
| | - Francesco Burzotta
- Department of Cardiovascular Sciences, Università Cattolica Del Sacro Cuore, Rome, Italy
| | - John F LaDisa
- Departments of Biomedical Engineering and Pediatrics - Division of Cardiology, Herma Heart Institute, Children's Wisconsin and the Medical College of Wisconsin, and the MARquette Visualization Lab, Marquette University, Milwaukee, Wisconsin, USA
| | - Paul Iaizzo
- Visible Heart Laboratories, Department of Surgery, University of Minnesota, Minnesota, USA
| | - Yoshinobu Murasato
- Department of Cardiology, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
| | - Gabriele Dubini
- Department of Chemistry, Materials and Chemical Engineering 'Giulio Natta', Politecnico di Milano, Milan, Italy
| | - Francesco Migliavacca
- Department of Chemistry, Materials and Chemical Engineering 'Giulio Natta', Politecnico di Milano, Milan, Italy
| | | | - Andrew Bicek
- Boston Scientific Inc, Marlborough, Massachusetts, USA
| | | | | | | | - Pamela J Boyers
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Jeffrey P Gold
- Interprofessional Experiential Center for Enduring Learning, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Daniel R Anderson
- Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - James E Tcheng
- Cardiovascular Division, Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina, USA
| | - John R Windle
- Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Habib Samady
- Georgia Heart Institute, Gainesville, Georgia, USA
| | - Farouc A Jaffer
- Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nihar R Desai
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Alexandra Lansky
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Carlos Mena-Hurtado
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Dawn Abbott
- Cardiovascular Institute, Warren Alpert Medical School at Brown University, Providence, Rhode Island, USA
| | - Emmanouil S Brilakis
- Center for Advanced Coronary Interventions, Minneapolis Heart Institute, Minneapolis, Minnesota, USA
| | - Jens Flensted Lassen
- Department of Cardiology B, Odense University Hospital, Odense, Syddanmark, Denmark
| | - Yves Louvard
- Institut Cardiovasculaire Paris Sud, Massy, France
| | - Goran Stankovic
- Department of Cardiology, Clinical Center of Serbia, Belgrade, Serbia
| | - Patrick W Serruys
- Department of Cardiology, National University of Ireland, Galway, Galway, Ireland
| | - Eric Velazquez
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Pierre Elias
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, New York, New York, USA
| | - Deepak L Bhatt
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - George Dangas
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yiannis S Chatzizisis
- Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA; Cardiovascular Biology and Biomechanics Laboratory (CBBL), Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA.
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Nopour R, Shanbezadeh M, Kazemi-Arpanahi H. Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2023; 12:16. [PMID: 37034879 PMCID: PMC10079178 DOI: 10.4103/jehp.jehp_20_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 04/26/2022] [Indexed: 06/19/2023]
Abstract
BACKGROUND Accurately predicting the intubation risk in COVID-19 patients at the admission time is critical to optimal use of limited hospital resources, providing customized and evidence-based treatments, and improving the quality of delivered medical care services. This study aimed to design a statistical algorithm to select the best features influencing intubation prediction in coronavirus disease 2019 (COVID-19) hospitalized patients. Then, using selected features, multiple artificial neural network (ANN) configurations were developed to predict intubation risk. MATERIAL AND METHODS In this retrospective single-center study, a dataset containing 482 COVID-19 patients who were hospitalized between February 9, 2020 and July 20, 2021 was used. First, the Phi correlation coefficient method was performed for selecting the most important features affecting COVID-19 patients' intubation. Then, the different configurations of ANN were developed. Finally, the performance of ANN configurations was assessed using several evaluation metrics, and the best structure was determined for predicting intubation requirements among hospitalized COVID-19 patients. RESULTS The ANN models were developed based on 18 validated features. The results indicated that the best performance belongs to the 18-20-1 ANN configuration with positive predictive value (PPV) = 0.907, negative predictive value (NPV) = 0.941, sensitivity = 0.898, specificity = 0.951, and area under curve (AUC) = 0.906. CONCLUSIONS The results demonstrate the effectiveness of the ANN models for timely and reliable prediction of intubation risk in COVID-19 hospitalized patients. Our models can inform clinicians and those involved in policymaking and decision making for prioritizing restricted mechanical ventilation and other related resources for critically COVID-19 patients.
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Affiliation(s)
- Raoof Nopour
- Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran
| | - Mostafa Shanbezadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
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Orooji A, Shanbehzadeh M, Mirbagheri E, Kazemi-Arpanahi H. Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19. BMC Infect Dis 2022; 22:923. [PMID: 36494613 PMCID: PMC9733380 DOI: 10.1186/s12879-022-07921-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The exponential spread of coronavirus disease 2019 (COVID-19) causes unexpected economic burdens to worldwide health systems with severe shortages in hospital resources (beds, staff, equipment). Managing patients' length of stay (LOS) to optimize clinical care and utilization of hospital resources is very challenging. Projecting the future demand requires reliable prediction of patients' LOS, which can be beneficial for taking appropriate actions. Therefore, the purpose of this research is to develop and validate models using a multilayer perceptron-artificial neural network (MLP-ANN) algorithm based on the best training algorithm for predicting COVID-19 patients' hospital LOS. METHODS Using a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020 to December 20, 2020 were analyzed. In this study, first, the correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at a P-value < 0.2 were used in model construction. Then, the prediction models were developed based on 12 training algorithms according to full and selected feature datasets (90% of the training, with 10% used for model validation). Afterward, the root mean square error (RMSE) was used to assess the models' performance in order to select the best ANN training algorithm. Finally, a total of 343 patients were used for the external validation of the models. RESULTS After implementing feature selection, a total of 20 variables were determined as the contributing factors to COVID-19 patients' LOS in order to build the models. The conducted experiments indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian regularization (BR) training algorithm for whole and selected features with an RMSE of 1.6213 and 2.2332, respectively. CONCLUSIONS MLP-ANN-based models can reliably predict LOS in hospitalized patients with COVID-19 using readily available data at the time of admission. In this regard, the models developed in our study can help health systems to optimally allocate limited hospital resources and make informed evidence-based decisions.
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Affiliation(s)
- Azam Orooji
- grid.464653.60000 0004 0459 3173Department of Medical Informatics, Department of Advanced Technologies, School of Medicine, North Khorasan University of Medical Science (NKUMS), North Khorasan, Iran
| | - Mostafa Shanbehzadeh
- grid.449129.30000 0004 0611 9408Department of Health Information Management, Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Esmat Mirbagheri
- grid.411746.10000 0004 4911 7066Department of Health Information Management, Iran University of Medical Sciences, Tehran, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Management, Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran , Department of Health Information Management, Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
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Fransvea P, Fransvea G, Liuzzi P, Sganga G, Mannini A, Costa G. Study and validation of an explainable machine learning-based mortality prediction following emergency surgery in the elderly: A prospective observational study. Int J Surg 2022; 107:106954. [PMID: 36229017 DOI: 10.1016/j.ijsu.2022.106954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/07/2022] [Accepted: 10/03/2022] [Indexed: 10/31/2022]
Abstract
INTRODUCTION The heterogeneity of procedures and the variety of comorbidities of the patients undergoing surgery in an emergency setting makes perioperative risk stratification, planning, and risk mitigation crucial. In this optic, Machine Learning has the capability of deriving data-driven predictions based on multivariate interactions of thousands of instances. Our aim was to cross-validate and test interpretable models for the prediction of post-operative mortality after any surgery in an emergency setting on elderly patients. METHODS This study is a secondary analysis derived from the FRAILESEL study, a multi-center (N = 29 emergency care units), nationwide, observational prospective study with data collected between 06-2017 and 06-2018 investigating perioperative outcomes of elderly patients (age≥65 years) undergoing emergency surgery. Demographic and clinical data, medical and surgical history, preoperative risk factors, frailty, biochemical blood examination, vital parameters, and operative details were collected and the primary outcome was set to the 30-day mortality. RESULTS Of the 2570 included patients (50.66% males, median age 77 [IQR = 13] years) 238 (9.26%) were in the non-survivors group. The best performing solution (MultiLayer Perceptron) resulted in a test accuracy of 94.9% (sensitivity = 92.0%, specificity = 95.2%). Model explanations showed how non-chronic cardiac-related comorbidities reduced activities of daily living, low consciousness levels, high creatinine and low saturation increase the risk of death following surgery. CONCLUSIONS In this prospective observational study, a robustly cross-validated model resulted in better predictive performance than existing tools and scores in literature. By using only preoperative features and by deriving patient-specific explanations, the model provides crucial information during shared decision-making processes required for risk mitigation procedures.
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Affiliation(s)
- Pietro Fransvea
- Emergency Surgery and Trauma, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Largo A. Gemelli 8, Rome, Italy The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, Pontedera, PI, Italy IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy Surgery Center, Colorectal Surgery Unit - Fondazione Policlinico Campus Bio-Medico, University Hospital of University Campus Bio-Medico of Rome, Rome, Italy
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10
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Gautam N, Ghanta SN, Clausen A, Saluja P, Sivakumar K, Dhar G, Chang Q, DeMazumder D, Rabbat MG, Greene SJ, Fudim M, Al'Aref SJ. Contemporary Applications of Machine Learning for Device Therapy in Heart Failure. JACC. HEART FAILURE 2022; 10:603-622. [PMID: 36049812 DOI: 10.1016/j.jchf.2022.06.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 05/31/2022] [Accepted: 06/16/2022] [Indexed: 06/15/2023]
Abstract
Despite a better understanding of the underlying pathogenesis of heart failure (HF), pharmacotherapy, surgical, and percutaneous interventions do not prevent disease progression in all patients, and a significant proportion of patients end up requiring advanced therapies. Machine learning (ML) is gaining wider acceptance in cardiovascular medicine because of its ability to incorporate large, complex, and multidimensional data and to potentially facilitate the creation of predictive models not constrained by many of the limitations of traditional statistical approaches. With the coexistence of "big data" and novel advanced analytic techniques using ML, there is ever-increasing research into applying ML in the context of HF with the goal of improving patient outcomes. Through this review, the authors describe the basics of ML and summarize the existing published reports regarding contemporary applications of ML in device therapy for HF while highlighting the limitations to widespread implementation and its future promises.
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Affiliation(s)
- Nitesh Gautam
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Sai Nikhila Ghanta
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Alex Clausen
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Prachi Saluja
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Kalai Sivakumar
- Division of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Gaurav Dhar
- Division of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Qi Chang
- Department of Computer Science, Rutgers University, The State University of New Jersey, Newark, New Jersey, USA
| | | | - Mark G Rabbat
- Department of Cardiology, Loyola University Medical Center, Maywood, Illinois, USA
| | - Stephen J Greene
- Department of Cardiology, Duke University Medical Center, Durham, North Carolina, USA; Duke Clinical Research Institute, Durham, North Carolina, USA
| | - Marat Fudim
- Department of Cardiology, Duke University Medical Center, Durham, North Carolina, USA; Duke Clinical Research Institute, Durham, North Carolina, USA
| | - Subhi J Al'Aref
- Division of Cardiology, Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
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Nopour R, Shanbehzadeh M, Kazemi-Arpanahi H. Predicting the Need for Intubation among COVID-19 Patients Using Machine Learning Algorithms: A Single-Center Study. Med J Islam Repub Iran 2022; 36:30. [PMID: 35999913 PMCID: PMC9386770 DOI: 10.47176/mjiri.36.30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 04/04/2022] [Indexed: 12/15/2022] Open
Abstract
Background: Owing to the shortage of ventilators, there is a crucial demand for an objective and accurate prognosis for 2019 coronavirus disease (COVID-19) critical patients, which may necessitate a mechanical ventilator (MV). This study aimed to construct a predictive model using machine learning (ML) algorithms for frontline clinicians to better triage endangered patients and priorities who would need MV.
Methods: In this retrospective single-center study, the data of 482 COVID-19 patients from February 9, 2020, to December 20, 2020, were analyzed by several ML algorithms including, multi-layer perception (MLP), logistic regression (LR), J-48 decision tree, and Naïve Bayes (NB). First, the most important clinical variables were identified using the Chi-square test at P < 0.01. Then, by comparing the ML algorithms' performance using some evaluation criteria, including TP-Rate, FP-Rate, precision, recall, F-Score, MCC, and Kappa, the best performing one was identified. Results: Predictive models were trained using 15 validated features, including cough, contusion, oxygen therapy, dyspnea, loss of taste, rhinorrhea, blood pressure, absolute lymphocyte count, pleural fluid, activated partial thromboplastin time, blood glucose, white cell count, cardiac diseases, length of hospitalization, and other underline diseases. The results indicated the J-48 with F-score = 0.868 and AUC = 0.892 yielded the best performance for predicting intubation requirement.
Conclusion: ML algorithms are potentials to improve traditional clinical criteria to forecast the necessity for intubation in COVID-19 in-hospital patients. Such ML-based prediction models may help physicians with optimizing the timing of intubation, better sharing of MV resources and personnel, and increase patient clinical status.
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Affiliation(s)
- Raoof Nopour
- Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran.,Department of Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
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Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Design of an artificial neural network to predict mortality among COVID-19 patients. INFORMATICS IN MEDICINE UNLOCKED 2022; 31:100983. [PMID: 35664686 PMCID: PMC9148440 DOI: 10.1016/j.imu.2022.100983] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 05/26/2022] [Accepted: 05/26/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction The fast pandemic of coronavirus disease 2019 (COVID-19) has challenged clinicians with many uncertainties and ambiguities regarding disease outcomes and complications. To deal with these uncertainties, our study aimed to develop and evaluate several artificial neural networks (ANNs) to predict the mortality risk in hospitalized COVID-19 patients. Material and methods The data of 1710 hospitalized COVID-19 patients were used in this retrospective and developmental study. First, a Chi-square test (P < 0.05), Eta coefficient (η > 0.4), and binary logistics regression (BLR) analysis were performed to determine the factors affecting COVID-19 mortality. Then, using the selected variables, two types of feed-forward (FF) models, including the back-propagation (BP) and distributed time delay (DTD) were trained. The models' performance was assessed using mean squared error (MSE), error histogram (EH), and area under the ROC curve (AUC-ROC) metrics. Results After applying the univariate and multivariate analysis, 13 variables were selected as important features in predicting COVID-19 mortality at P < 0.05. A comparison of the two ANN architectures using the MSE showed that the BP-ANN (validation error: 0.067, most of the classified samples having 0.049 and 0.05 error rates, and AUC-ROC: 0.888) was the best model. Conclusions Our findings show the acceptable performance of ANN for predicting the risk of mortality in hospitalized COVID-19 patients. Application of the developed ANN-based CDSS in a real clinical environment will improve patient safety and reduce disease severity and mortality.
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Rubinger L, Gazendam A, Ekhtiari S, Bhandari M. Machine learning and artificial intelligence in research and healthcare ✰,✰✰. Injury 2022:S0020-1383(22)00076-6. [PMID: 35135685 DOI: 10.1016/j.injury.2022.01.046] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 01/29/2022] [Indexed: 02/02/2023]
Abstract
Artificial intelligence (AI) is a broad term referring to the application of computational algorithms that can analyze large data sets to classify, predict, or gain useful conclusions. Under the umbrella of AI is machine learning (ML). ML is the process of building or learning statistical models using previously observed real world data to predict outcomes, or categorize observations based on 'training' provided by humans. These predictions are then applied to future data, all the while folding in the new data into its perpetually improving and calibrated statistical model. The future of AI and ML in healthcare research is exciting and expansive. AI and ML are becoming cornerstones in the medical and healthcare-research domains and are integral in our continued processing and capitalization of robust patient EMR data. Considerations for the use and application of ML in healthcare settings include assessing the quality of data inputs and decision-making that serve as the foundations of the ML model, ensuring the end-product is interpretable, transparent, and ethical concerns are considered throughout the development process. The current and future applications of ML include improving the quality and quantity of data collected from EMRs to improve registry data, utilizing these robust datasets to improve and standardized research protocols and outcomes, clinical decision-making applications, natural language processing and improving the fundamentals of value-based care, to name only a few.
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Affiliation(s)
- Luc Rubinger
- Division of Orthopaedics, Department of Surgery, McMaster University, Hamilton, ON Canada; Centre for Evidence-Based Orthopaedics, 293 Wellington St. N, Suite 110, Hamilton, ON L8L 8E7 Canada.
| | - Aaron Gazendam
- Division of Orthopaedics, Department of Surgery, McMaster University, Hamilton, ON Canada; Centre for Evidence-Based Orthopaedics, 293 Wellington St. N, Suite 110, Hamilton, ON L8L 8E7 Canada
| | - Seper Ekhtiari
- Division of Orthopaedics, Department of Surgery, McMaster University, Hamilton, ON Canada; Centre for Evidence-Based Orthopaedics, 293 Wellington St. N, Suite 110, Hamilton, ON L8L 8E7 Canada
| | - Mohit Bhandari
- Division of Orthopaedics, Department of Surgery, McMaster University, Hamilton, ON Canada; Centre for Evidence-Based Orthopaedics, 293 Wellington St. N, Suite 110, Hamilton, ON L8L 8E7 Canada
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Varzaneh ZA, Orooji A, Erfannia L, Shanbehzadeh M. A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method. INFORMATICS IN MEDICINE UNLOCKED 2022; 28:100825. [PMID: 34977330 PMCID: PMC8712462 DOI: 10.1016/j.imu.2021.100825] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/13/2021] [Accepted: 12/14/2021] [Indexed: 12/14/2022] Open
Abstract
Background Predicting severe respiratory failure due to COVID-19 can help triage patients to higher levels of care, resource allocation and decrease morbidity and mortality. The need for this research derives from the increasing demand for innovative technologies to overcome complex data analysis and decision-making tasks in critical care units. Hence the aim of our paper is to present a new algorithm for selecting the best features from the dataset and developing Machine Learning(ML) based models to predict the intubation risk of hospitalized COVID-19 patients. Methods In this retrospective single-center study, the data of 1225 COVID-19 patients from February 9, 2020, to July 20, 2021, were analyzed by several ML algorithms which included, Decision Tree(DT), Support Vector Machine (SVM), Multilayer perceptron (MLP), and K-Nearest Neighbors(K-NN). First, the most important predictors were identified using the Horse herd Optimization Algorithm (HOA). Then, by comparing the ML algorithms' performance using some evaluation criteria, the best performing one was identified. Results Predictive models were trained using 12 validated features. Also, it found that proposed DT-based predictive model enables a reasonable level of accuracy (=93%) in predicting the risk of intubation among hospitalized COVID-19 patients. Conclusions The experimental results demonstrate the effectiveness of the proposed meta-heuristic feature selection technique in combining with DT model in predicting intubation risk for hospitalized patients with COVID-19. The proposed model have the potential to inform frontline clinicians with quantitative and non-invasive tool to assess illness severity and to identify high risk patients.
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Affiliation(s)
- Zahra Asghari Varzaneh
- Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Azam Orooji
- Department of Advanced Technologies, School of Medicine, North Khorasan University of Medical Science (NKUMS), North Khorasan, Iran
| | - Leila Erfannia
- Department of Health Information Management, School of Management and Medical Informatics, Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
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Moulaei K, Shanbehzadeh M, Mohammadi-Taghiabad Z, Kazemi-Arpanahi H. Comparing machine learning algorithms for predicting COVID-19 mortality. BMC Med Inform Decis Mak 2022; 22:2. [PMID: 34983496 PMCID: PMC8724649 DOI: 10.1186/s12911-021-01742-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 12/28/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The coronavirus disease (COVID-19) hospitalized patients are always at risk of death. Machine learning (ML) algorithms can be used as a potential solution for predicting mortality in COVID-19 hospitalized patients. So, our study aimed to compare several ML algorithms to predict the COVID-19 mortality using the patient's data at the first time of admission and choose the best performing algorithm as a predictive tool for decision-making. METHODS In this study, after feature selection, based on the confirmed predictors, information about 1500 eligible patients (1386 survivors and 144 deaths) obtained from the registry of Ayatollah Taleghani Hospital, Abadan city, Iran, was extracted. Afterwards, several ML algorithms were trained to predict COVID-19 mortality. Finally, to assess the models' performance, the metrics derived from the confusion matrix were calculated. RESULTS The study participants were 1500 patients; the number of men was found to be higher than that of women (836 vs. 664) and the median age was 57.25 years old (interquartile 18-100). After performing the feature selection, out of 38 features, dyspnea, ICU admission, and oxygen therapy were found as the top three predictors. Smoking, alanine aminotransferase, and platelet count were found to be the three lowest predictors of COVID-19 mortality. Experimental results demonstrated that random forest (RF) had better performance than other ML algorithms with accuracy, sensitivity, precision, specificity, and receiver operating characteristic (ROC) of 95.03%, 90.70%, 94.23%, 95.10%, and 99.02%, respectively. CONCLUSION It was found that ML enables a reasonable level of accuracy in predicting the COVID-19 mortality. Therefore, ML-based predictive models, particularly the RF algorithm, potentially facilitate identifying the patients who are at high risk of mortality and inform proper interventions by the clinicians.
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Affiliation(s)
- Khadijeh Moulaei
- Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Zahra Mohammadi-Taghiabad
- Department of Health Information Management, School of Health Management and Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran.
- Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran.
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Fawcett WJ, Klein AA. Anaesthesia and peri-operative medicine over the next 25 years. Anaesthesia 2021; 76:1416-1420. [PMID: 34333762 DOI: 10.1111/anae.15552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/04/2021] [Indexed: 12/31/2022]
Affiliation(s)
- W J Fawcett
- Department of Anaesthesia, Royal Surrey County NHS Foundation Trust, Guildford, UK
| | - A A Klein
- Department of Anaesthesia and Intensive Care, Royal Papworth Hospital, Cambridge, UK
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Langarizadeh M, HosseiniNezhad M, Hosseini S. Mortality prediction of mitral valve replacement surgery by machine learning. Res Cardiovasc Med 2021. [DOI: 10.4103/rcm.rcm_50_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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